A Design of a low-pass FIR filter using Hamming Window Functions in Matlab

Adjei A S Hannah ,George K Agordzo

doi:10.7176/ceis/11-2-04

Abstract

The reduction and filtering of the input components of an original signal in one or more frequency bands using a finite impulse response, better known as FIR, is designed using a function of the Hamming window. Although there are various window functions such as the Blackman window function, the Hanning window function and the rectangular window functions that can be used as digital filters, the Hamming window function was used in this study for the reason of its minimum damping/decibel of the stopband with a reduced transition bandwidth. Among the other three widow functions that can be used, the Blackman window function is closest to the Hamming window function in terms of minimum bandstop attenuation/decibel, since both have a dB value greater than -50. However, in terms of transition bandwidth (Δω), the Hamming window has a narrower bandwidth than the Blackman window, making it more appropriate to use in this FIR filter design. This type of filter is important for analyzing the different types of signals that are essential in a world where digital filters play a major role in DSP applications. This research paper offers a Matlab-based low-pass FIR digital filter that uses Hamming window functions. Keywords: FIR filters, Hamming window, Blackman window Hanning window, Matlab. DOI: 10.7176/CEIS/11-2-04 Publication date: February 29 th 2020

Highlights

Matlab offers several options for designing digital filters including algorithms filtering and a graphical user interface function called SPtool
FIR filters (Finite Impulse Response Filters) among the main digital filter types used in various DSP (Digital Signal Processing) applications such as digital filters are very flexible and portable
A full extension of the sampling design technique is to make all frequency response points variable and to optimize their position to minimize a certain degree of error between the desired samples and the estimated frequency response. One of these techniques was developed by Rabiner [2], who designed the equivalent of 7 FIR filters that minimize the maximum absolute value of the error between the desired response and the estimated FIR filter

Summary

Introduction

Matlab offers several options for designing digital filters including algorithms filtering and a graphical user interface function called SPtool. Various filter design algorithms for FIR filters are available in Matlab. This article describes the various options in Matlab and provides examples of low-pass, high-pass, and band-pass filter designs. Digital filter design techniques are broadly used in various areas. FIR filters (Finite Impulse Response Filters) among the main digital filter types used in various DSP (Digital Signal Processing) applications such as digital filters are very flexible and portable. The digital filter has minimal or insignificant interferences, stores, manage and reduces downtime. Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) both use digital filters. An advantage of FIR over IIR is that it has greater flexibility in controlling the shape of your response than IIR filters

Methods

Discussion

Conclusion